repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Muflhi01/naturalproofs | [
"2b149e144aa55b6e4154420f0f21b6c48d5e5fff"
] | [
"naturalproofs/encoder_decoder/predict.py"
] | [
"import pytorch_lightning as pl\nimport argparse\nimport os\nimport pickle\nfrom pathlib import Path\nimport naturalproofs.encoder_decoder.model as mutils\nimport naturalproofs.encoder_decoder.utils as utils\nfrom naturalproofs.encoder_decoder.analyze import analyze, analyze_rankings\nimport numpy as np\nimport tor... | [
[
"torch.tensor",
"torch.no_grad",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jerrylzy/RNN_Jazzy_Haydn | [
"4c336003eec921c8a59c6b6d8abde39c32145c55"
] | [
"music_utils.py"
] | [
"from __future__ import print_function\nimport tensorflow as tf\nimport keras.backend as K\nfrom keras.layers import RepeatVector\nimport sys\nfrom music21 import *\nimport numpy as np\nfrom grammar import *\nfrom preprocess import *\nfrom qa import *\n\n\ndef data_processing(corpus, values_indices, m = 60, Tx = 30... | [
[
"numpy.asarray",
"numpy.swapaxes",
"numpy.zeros",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MichaelArbel/OT-sync | [
"0b8308375b0064a9ada3f8741f04551a3ba29b63"
] | [
"core/aligned_pc_generator.py"
] | [
"from open3d import *\nimport time\nimport numpy as np\nfrom subprocess import STDOUT, check_output\nfrom open3d import *\nimport copy\nimport uuid\nimport random\nimport shutil\nimport os\nfrom os.path import isdir, join, exists\nimport pdb\nimport scipy\nfrom scipy import io\n\n\nif __name__ == \"__main__\":\n\t#... | [
[
"numpy.dot",
"scipy.io.loadmat",
"numpy.loadtxt",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
muszka95/M2U-Net | [
"855215b9a8195a6bcd4516b0d5dc15eb7bba2ae2"
] | [
"unet.py"
] | [
"# MIT License\n\n# Copyright (c) 2018 Joris\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, m... | [
[
"torch.nn.Sequential",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.functional.avg_pool2d",
"torch.nn.Upsample",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
enlipleai/korquad-challenge | [
"9807dcb0f0052c389dfd02cd5fa731c904a843f9"
] | [
"src/models/modeling_bert.py"
] | [
"# coding=utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport copy\nimport json\nimport logging\nimport math\nimport torch\nimport torch.nn as nn\n\nfrom torch.nn import CrossEntropyLoss, Dropout, Embedding, Softmax\n\nlogger = logging.getL... | [
[
"torch.nn.Softmax",
"torch.sigmoid",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Embedding",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.matmul",
"torch.pow",
"torch.nn.init.normal_",
"torch.arange"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bpotvin-bccrc/colossus | [
"fa5ca7ce4cfe794c7d2167acb868aa9167988941"
] | [
"core/views.py"
] | [
"\"\"\"\nCreated on May 16, 2016\n\n@author: Jafar Taghiyar (jtaghiyar@bccrc.ca)\n\nUpdated by Spencer Vatrt-Watts (github.com/Spenca)\n\"\"\"\n\nimport os\nimport csv\nimport collections\nimport re\nimport subprocess\nimport json\nfrom datetime import timedelta\n\nimport requests\nfrom jira import JIRAError\n\n#==... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
AndSt/CarND-Advanced-Lane-Lines | [
"7eec0d777c6d918bcc44b6177c1cc3f94af61ed0"
] | [
"src/line.py"
] | [
"from src.utils import diff\nimport numpy as np\n\n# Define a class to receive the characteristics of each line detection\nclass Line():\n def __init__(self):\n # was the line detected in the last iteration?\n self.detected = False\n # x values of the last n fits of the line\n self.re... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dexterchan/beam | [
"01e500c2dd0d699aea0434154b69fd59d824700f"
] | [
"sdks/python/apache_beam/examples/complete/distribopt_test.py"
] | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beargolden/DP-LinkNet | [
"cba8c27172099807639bde36a211129d11d557df"
] | [
"test.py"
] | [
"import os\nfrom time import time\n\nimport cv2\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable as V\n\n# from networks.unet import UNet\n# from networks.dunet import DUNet\nfrom networks.dplinknet import LinkNet34, DLinkNet34, DPLinkNet34\nfrom utils import get_patches, stitch_together\n\nBA... | [
[
"numpy.rot90",
"torch.Tensor",
"torch.load",
"numpy.concatenate",
"torch.cuda.device_count",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MedicalVisionGroup/interlacer | [
"60c14782729031a2af48c27fddb649d37cdca0e9"
] | [
"interlacer/data_generator.py"
] | [
"import os\n\nimport numpy as np\nimport tensorflow as tf\nfrom scipy import ndimage\nfrom skimage.transform import resize\nfrom tensorflow import keras\n\nfrom scripts import filepaths\nfrom interlacer import motion, utils\n\n\ndef normalize_slice(sl_data):\n \"\"\"Normalize slice to z-scores across dataset.\n\... | [
[
"numpy.expand_dims",
"numpy.random.random",
"numpy.random.choice",
"numpy.asarray",
"numpy.ones",
"numpy.int",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.random.normal",
"numpy.append",
"numpy.fft.ifftshift",
"numpy.random.uniform",
"numpy.repeat",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomasaarholt/plato | [
"ea772028cb8bfc56e8ba482cca0ac5407919626f"
] | [
"plato/draw/matplotlib/Scene.py"
] | [
"import contextlib\n\nimport matplotlib.pyplot as pp\nfrom matplotlib.collections import PatchCollection\nimport numpy as np\n\nfrom ... import draw\n\n@contextlib.contextmanager\ndef manage_matplotlib_interactive():\n was_interactive = pp.isinteractive()\n pp.ioff()\n\n yield None\n if was_interactive:... | [
[
"matplotlib.collections.PatchCollection",
"matplotlib.pyplot.rcParams.get",
"matplotlib.pyplot.isinteractive",
"numpy.concatenate",
"matplotlib.pyplot.ioff",
"numpy.atleast_2d",
"numpy.argsort",
"numpy.array",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klmcguir/tensortools | [
"38262f5bad9d3171286e34e5f15d196752dda939"
] | [
"tensortools/optimize/ncp_bcd.py"
] | [
"\"\"\"\nCP decomposition by classic alternating least squares (ALS).\n\nAuthor: N. Benjamin Erichson <erichson@uw.edu> and Alex H. Williams\n\"\"\"\n\nimport numpy as np\nimport scipy as sci\nfrom scipy import linalg\n\nfrom tensortools.operations import unfold, khatri_rao\nfrom tensortools.tensors import KTensor\... | [
[
"scipy.sum",
"numpy.arange",
"scipy.maximum",
"numpy.ones",
"scipy.sqrt",
"scipy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
HUISUN24/pycalphad | [
"cf126ce54f5e30abd8c9f05e3761946001cb3245"
] | [
"pycalphad/plot/triangular.py"
] | [
"\"\"\"\nRegister a ``'triangular'`` projection with matplotlib to plot diagrams on\ntriangular axes.\n\nUsers should not have to instantiate the TriangularAxes class directly.\nInstead, the projection name can be passed as a keyword argument to\nmatplotlib.\n\n>>> import matplotlib.pyplot as plt\n>>> import numpy ... | [
[
"matplotlib.projections.register_projection",
"matplotlib.spines.Spine.linear_spine",
"numpy.sqrt",
"matplotlib.axis.YAxis",
"numpy.arange",
"matplotlib.axis.XAxis",
"matplotlib.transforms.BboxTransformTo",
"matplotlib.ticker.NullLocator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gjacquenot/vtkplotter | [
"fbed73d6ed3f049dca3421c64e2e82ac1eef69a8"
] | [
"examples/other/voronoi3d.py"
] | [
"'''\nVoronoi in 3D with Voro++ library.\n'''\nfrom vtkplotter import voronoi3D, Points, show, settings\nimport numpy as np\n\n#settings.voro_path = '/g/sharpeba/software/bin'\n\nN = 2000\nnuclei = np.random.rand(N, 3) - (0.5,0.5,0.5)\nncl = Points(nuclei).clean(0.1) # clean makes points evenly spaced\nnuclei = ncl... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saeedmhz/Sarc-Graph | [
"2746b2a88543487c0a501992c0cb6f342b16420e"
] | [
"analysis_tools.py"
] | [
"import pickle\nimport cv2\nfrom skimage.filters import threshold_otsu, threshold_local\nfrom skimage import measure\nfrom scipy import ndimage\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport glob\nimport imageio\nimport scipy\nfrom scipy import signal\nfrom skimage.feature impor... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"scipy.cluster.hierarchy.leaves_list",
"scipy.signal.find_peaks",
"numpy.sqrt",
"numpy.linspace",
"numpy.dot",
"numpy.asarray",
"matplotlib.pyplot.imread",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.arctan2"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5... |
KongBOy/analyze_16_code_iin | [
"fa0d2d1cfa6fc0b813b902cd3474145a045fbb34"
] | [
"iin/models/clf.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\nfrom edflow.util import retrieve\n\nfrom iin.models.ae import FeatureLayer, DenseEncoderLayer, weights_init\n\n\nclass Distribution(object):\n def __init__(self, value):\n self.value = value\n\n def sample(self):\n return self.value\n\n ... | [
[
"torch.nn.Linear",
"torch.nn.ModuleList"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CashabackLab/DataVisualization | [
"91b2a2d6020ae2fb5b8277f5c7bca69d620be1cb"
] | [
"data_visualization/plot_functions.py"
] | [
"import matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom scipy.stats import spearmanr\r\nfrom matplotlib.lines import Line2D\r\nfrom tqdm.notebook import tqdm\r\nfrom PIL import Image\r\n \r\ndef Custom_Legend(ax, labels: \"List[String]\", colors : \"List of color names\", ncol = 1... | [
[
"numpy.min",
"matplotlib.pyplot.imread",
"scipy.stats.spearmanr",
"matplotlib.lines.Line2D",
"numpy.cumsum",
"numpy.sort",
"numpy.max",
"matplotlib.cbook.get_sample_data"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
luiarthur/cytof5 | [
"6b4df5e9fd94bfd586e96579b8c618fdf6f913ed",
"6b4df5e9fd94bfd586e96579b8c618fdf6f913ed"
] | [
"sandbox/pytorch/gmm.py",
"sims/cb/cytofpy/attempt1/main.py"
] | [
"# https://www.kaggle.com/aakashns/pytorch-basics-linear-regression-from-scratch\n# https://angusturner.github.io/generative_models/2017/11/03/pytorch-gaussian-mixture-model.html\n# https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_custom_function.html\n\nimport torch\nimport time\nimport math\... | [
[
"torch.optim.Adam",
"torch.mean",
"torch.softmax",
"torch.empty",
"torch.manual_seed",
"torch.randn",
"torch.tensor",
"torch.exp",
"torch.log_softmax",
"torch.std",
"torch.set_num_threads",
"torch.log",
"torch.device"
],
[
"matplotlib.pyplot.imshow",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
viclafargue/cuml | [
"063c2c6f25354db46e9aaa18e27fb3c7edcfd093"
] | [
"python/cuml/utils/numba_utils.py"
] | [
"#\n# Copyright (c) 2018-2020, NVIDIA CORPORATION.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TevenLeScao/BasicSR | [
"1a7bd8754de00f3a9c9f2031acfc447350459ea0"
] | [
"codes/models/modules/reac_diff_arch.py"
] | [
"import functools\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport models.modules.module_util as mutil\nfrom anode.anode.odeblock import make_odeblock\nfrom anode.models.sr_trunk import SRTrunk\nfrom torchdiffeq._impl.reaction_diffusion import ReactionDiffusion\nfrom torchdiffeq._impl.c... | [
[
"torch.nn.LeakyReLU",
"torch.nn.functional.interpolate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ethch18/specializing-multilingual | [
"353fbe0aac88066207db85909554a205a803921c"
] | [
"scripts/eval/summarize_mtlall_sigtest.py"
] | [
"import collections\nimport json\nimport os\nimport sys\n\nimport numpy as np\nimport scipy.stats as scp\n\noptions = sys.argv[1].split(\"|\")\n\nresults_dir = \"/gscratch/cse/echau18/lr-ssmba/result_files_mtl\"\nraw_results_dir = \"/gscratch/cse/echau18/lr-ssmba/result_files_tva\"\n\ntasks = {\n \"pos\": \"accu... | [
[
"numpy.std",
"numpy.array",
"scipy.stats.ttest_1samp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
liuruoze/mini-AlphaStar | [
"cf9de2507d526a5fb8ef67676aab2ffb92738640"
] | [
"alphastarmini/core/arch/arch_model.py"
] | [
"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n\" ArchModel.\"\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.optim import Adam, RMSprop\r\n\r\nfrom alphastarmini.core.arch.scalar_encoder import ScalarEncoder\r\nfrom alphastarmini.core.arch.entity_encoder imp... | [
[
"torch.ones",
"torch.autograd.set_detect_anomaly",
"torch.zeros",
"torch.cat",
"torch.randn",
"torch.isnan",
"torch.unsqueeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aimeeu/Udacity-AIProgrammingWithPython | [
"4734a50a52cbbd00e19cc03eec62dadab1976452"
] | [
"final-project-flower-classification/helper.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# DEVELOPER: Aimee Ukasick\n# DATE CREATED: 25 May 2018\n# PURPOSE: common functions for train.py and predict.py\n\n##\n\nfrom collections import OrderedDict\nfrom torch import nn\n\n\ndef create_densenet_classifier(hidden_units):\n classifier = nn.Sequential(O... | [
[
"torch.nn.Dropout",
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.LogSoftmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bhishanpdl/Mass_Estimation | [
"63d6361023057290408fc6ef129558f83b913eaf"
] | [
"a02_add_star_wcs/a01_create_star/create_stars.py"
] | [
"#!python\n# -*- coding: utf-8 -*-#\n\"\"\"\nCreate convolved star fits files.\n\nAuthor : Bhishan Poudel\nDate : May 10, 2018\n\"\"\"\n# Imports\nimport numpy as np\nfrom astropy.io import fits\n\ndef create_fitsfile(star_positions,NAXISn,star_fits,star_value):\n # NOTE: from numpy to ds9 image: x,y ==> y+1, ... | [
[
"numpy.zeros",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RossFabricant/omegapoint | [
"b0a8c419a64395d62976dc39847b094b4f85d47c"
] | [
"omegapoint/utils.py"
] | [
"from datetime import date, timedelta\nimport pandas as pd\nfrom inspect import getfullargspec\n\n\ndef date_range(start, stop, skip=1):\n daycount = (stop - start).days\n for i in range(0, daycount+1, skip):\n yield start + timedelta(days=i)\n\n\ndef weekdays(start, stop, skip=1):\n return [d for d... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Piyush-97/metrics | [
"44e3ffbe024980a07fc4e4f13b58e46f03e4f3ba"
] | [
"torchmetrics/audio/snr.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
medric49/semantic_analyzer | [
"bd6112bf526046a9e2a3983cf198a6f2945a3ecd"
] | [
"same_analyze.py"
] | [
"import argparse\nimport sys\nfrom sys import stdin\nimport torch\nfrom transformers import BertTokenizer\nimport utils\nfrom models import BERTQuestionAnalyzer\n\n\ndef analyze(model, tokenizer, question1, question2):\n question = torch.tensor(tokenizer.encode(question1 + '|||' + question2), dtype=torch.long, d... | [
[
"torch.softmax",
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DamianAgi/molecool | [
"f90d0f4a9c993702096194f5aeea4f6569bc1741"
] | [
"molecool/functions.py"
] | [
"\"\"\"Provide the primary functions.\"\"\"\n\"\"\"\nfunctions.py\nA python package for analyzing xyz files\n\nHandles the primary functions\n\nthe source code goes here\n\"\"\"\n#import relevant files\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef ... | [
[
"numpy.dot",
"numpy.linspace",
"numpy.degrees",
"numpy.linalg.norm",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RussellRC/tensorflow | [
"a230417c58c258b2417225c739a1e5f0890491e6"
] | [
"tensorflow/contrib/layers/python/layers/layers_test.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.nn_ops.conv2d_transpose",
"numpy.all",
"tensorflow.python.ops.variables.Variable",
"tensorflow.contrib.framework.python.ops.variables.get_variables",
"tensorflow.python.ops.init_ops.constant_initializer",
"tensorflow.python.ops.gradients_impl.gradients",
"numpy.w... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
munoztd0/AI_games | [
"de2a45b1a68b26b21b8efbc140c679b5ff90cb9a"
] | [
"Game_Life/env/lib/python3.7/site-packages/numpy/distutils/ccompiler.py"
] | [
"import os\nimport re\nimport sys\nimport shlex\nimport time\nimport subprocess\nfrom copy import copy\nfrom distutils import ccompiler\nfrom distutils.ccompiler import (\n compiler_class, gen_lib_options, get_default_compiler, new_compiler,\n CCompiler\n)\nfrom distutils.errors import (\n DistutilsExecErr... | [
[
"numpy.distutils.misc_util.get_num_build_jobs",
"numpy.distutils.log.debug",
"numpy.distutils.misc_util.sanitize_cxx_flags",
"numpy.distutils.misc_util.is_sequence",
"numpy.distutils.misc_util._commandline_dep_string",
"numpy.distutils.misc_util.mingw32",
"numpy.distutils.log.warn",
... | [
{
"matplotlib": [],
"numpy": [
"1.24",
"1.22",
"1.23"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avocardio/MLinPractice | [
"db090d287908acdf1112df64c02e51830df58c90"
] | [
"code/feature_extraction/hashtag_count.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCount number of hashtags from column 'hashtags'.\n\nCreated on Wed Sep 29 12:22:13 2021\n\n@author: mkalcher, magmueller, shagemann\n\"\"\"\n\nimport numpy as np\nimport ast\nfrom code.feature_extraction.feature_extractor import FeatureExtractor\n\n\nclass H... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tigerhawkvok/autogluon | [
"722a1b6ca8b629e04714fc228895964dd4307830"
] | [
"autogluon/utils/tabular/ml/trainer/abstract_trainer.py"
] | [
"import copy, time, traceback, logging\nfrom typing import List\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom collections import defaultdict\nfrom sklearn.model_selection import train_test_split\n\nfrom ..constants import BINARY, MULTICLASS, REGRESSION\nfrom ...utils.loaders i... | [
[
"pandas.concat",
"sklearn.model_selection.train_test_split",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ArlindKadra/xgboost | [
"fd917a1c0d4eed901629553134e1a139cd0adb9d"
] | [
"worker.py"
] | [
"from copy import deepcopy\nfrom functools import partial\nimport os\nfrom typing import Dict, Tuple, Union\n\nfrom catboost import CatBoostClassifier, metrics\nimport ConfigSpace as cs\nfrom hpbandster.core.worker import Worker\nimport numpy as np\nfrom pytorch_tabnet.tab_model import TabNetClassifier\nfrom sklear... | [
[
"sklearn.metrics.balanced_accuracy_score",
"numpy.array",
"numpy.argmax",
"torch.manual_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sashank27/sunpy | [
"72fbed58a47ba599c8729a955ddaf874f8b9b5b4",
"72fbed58a47ba599c8729a955ddaf874f8b9b5b4"
] | [
"sunpy/image/tests/test_transform.py",
"sunpy/instr/tests/test_aia.py"
] | [
"import numpy as np\nimport pytest\nimport skimage.data as images\nfrom skimage import transform as tf\n\nfrom sunpy.image.transform import affine_transform\nfrom sunpy.util import SunpyUserWarning\n\n# Tolerance for tests\nRTOL = 1.0e-10\n\n\n@pytest.fixture\ndef original():\n # Test image\n return images.ca... | [
[
"numpy.rot90",
"numpy.radians",
"numpy.allclose",
"numpy.asarray",
"numpy.isnan",
"numpy.issubdtype",
"numpy.cos",
"numpy.sin",
"numpy.any",
"numpy.array",
"numpy.zeros",
"numpy.roll",
"numpy.isclose"
],
[
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kiraplenkin/reports_lib | [
"e13687dc0717cf98638bc8af1184b15369331298"
] | [
"src/reports/core/functions.py"
] | [
"from datetime import date\nimport pandas as pd\nimport numpy as np\nfrom typing import Union\nfrom scipy.sparse.construct import rand\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import roc_auc_score\n\n\ndef features_describe(\n ... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.log",
"sklearn.linear_model.LogisticRegression",
"numpy.unique",
"pandas.DataFrame",
"numpy.mean",
"pandas.DataFrame.from_dict",
"numpy.array",
"numpy.sum",
"numpy.isin",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
nataliejpg/QuantumResourceEstimation | [
"32154be096cdbc2b01ceb0647658e469360d3079"
] | [
"exact_diagonalisation/ed_hubbard.py"
] | [
"import numpy as np\nimport scipy\nimport scipy.special\nimport scipy.sparse.linalg as LA\nimport scipy.sparse as sparse\nimport copy\nimport warnings\n\n\ndef get_1d_coords(p, i, j):\n \"\"\"\n Finds index of site in 1d chain from 2d lattics based on snake\n decomposition, ie 1d coords on 2d lattice look ... | [
[
"numpy.imag",
"numpy.sum",
"numpy.conj",
"numpy.linspace",
"scipy.sparse.csr_matrix",
"numpy.sin",
"numpy.real",
"numpy.mean",
"numpy.shape",
"numpy.array",
"scipy.sparse.linalg.eigsh",
"numpy.empty",
"numpy.conjugate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
wawachen/PyRep | [
"e2cd9a1a7d1a6cee402f7a903ff99df5d93ea407"
] | [
"pyrep/robots/configuration_paths/arm_configuration_path.py"
] | [
"from pyrep.backend import sim\nfrom pyrep.robots.configuration_paths.configuration_path import (\n ConfigurationPath)\nimport numpy as np\nfrom typing import List, Optional\n\n\nclass ArmConfigurationPath(ConfigurationPath):\n \"\"\"A path expressed in joint configuration space.\n\n Paths are retrieved fr... | [
[
"numpy.square",
"numpy.max",
"numpy.array",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BenBBear/MNIST-XNORNet | [
"86443b94f7982af4a22d49d3e2ff8755117aacd5"
] | [
"MNIST.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\ndef bias_variable(shape):\n ... | [
[
"tensorflow.matmul",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.truncated_normal",
"tensorflow.InteractiveSession",
"tensorflow.Variable",
"tensorflow.constant",
"tensorflow.nn.max_pool",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.placeholder",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AmbiTyga/Poki-News | [
"005bb1afd65d3ef123add30f77321463ca91d879"
] | [
"Similar-Articles/testing/similarity.py"
] | [
"from flask import Flask,render_template,url_for,request\nfrom flask_bootstrap import Bootstrap\nfrom newspaper import Article\nimport pandas as pd\nimport numpy as np\nimport pickle\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklea... | [
[
"sklearn.metrics.pairwise.cosine_similarity",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ndawlab/vectorRPE | [
"d4cd6734b6d71e93e66f2e98edd745951866d8ac"
] | [
"figures/utils/cnnlstm_analysis_utils.py"
] | [
"\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport numpy as np\nimport pandas as pd\nfrom mpl_toolkits.mplot3d import Axes3D\nimport os\nimport zipfile\nimport io\n\n\n#### DATA MANIPULATION\n\n\ndef get_params_from_zip(load_path):\n model_file = zipfile.ZipFile(load_path + '.zip', \"r\")\n paramet... | [
[
"numpy.dot",
"numpy.split",
"numpy.squeeze",
"numpy.cumsum",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.nanmean",
"numpy.digitize",
"numpy.where",
"matplotlib.pyplot.gca",
"numpy.linalg.svd",
"numpy.hstack",
"numpy.unique",
"numpy.arange... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Asichurter/MalFusionFSL | [
"713bf64cc07a3489f42941fd2299837075575ac0",
"713bf64cc07a3489f42941fd2299837075575ac0"
] | [
"utils/magic.py",
"comp/nn/fusion/attention_fusion.py"
] | [
"from time import time\nimport random as rd\nimport numpy as np\n\nmagic = 7355608\nmagic_list_size = 100000\n\ndef magicSeed():\n return int((time()*100000000)%magic)\n\ndef magicList():\n return [i for i in range(magic)]\n\ndef randList(num, min_=0, max_=magic, seed=None, allow_duplicate=True):\n assert ... | [
[
"numpy.random.permutation",
"numpy.random.seed"
],
[
"torch.nn.Dropout",
"torch.cat",
"torch.sum",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DetectionBLWX/YOLOv3 | [
"0a9787670d109ef7d7fb668550e5715749f1ef5a"
] | [
"modules/utils/misc.py"
] | [
"'''\nFunction:\n\tsome util functions used for many module files\nAuthor:\n\tCharles\n'''\nimport os\nimport torch\nimport logging\nfrom torch.nn.utils import clip_grad\n\n\n'''check the existence of dirpath'''\ndef checkDir(dirpath):\n\tif not os.path.exists(dirpath):\n\t\tos.mkdir(dirpath)\n\t\treturn False\n\tr... | [
[
"torch.load",
"torch.nn.utils.clip_grad.clip_grad_norm_",
"torch.exp",
"torch.log",
"torch.stack",
"torch.clamp",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
crownk1997/plato | [
"56eecedd291f9dc9de9d101c9d87dea320dfacd4"
] | [
"tests/data_tests.py"
] | [
"\"\"\"\nTesting multi-modal datasources of Plato framework.\n\"\"\"\n\nimport os\nimport unittest\n\nos.environ['config_file'] = 'tests/TestsConfig/flickr30k_entities.yml'\n\n# os.environ['config_file'] = 'tests/TestsConfig/coco.yml'\n\n# os.environ['config_file'] = 'tests/TestsConfig/referitgame.yml'\n\n# os.envi... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hsim13372/pennylane | [
"1fae4c3412d60e1a792836551d7071f0ffd0fae0",
"1fae4c3412d60e1a792836551d7071f0ffd0fae0"
] | [
"pennylane/interfaces/torch.py",
"tests/qnodes/test_qnode_jacobian.py"
] | [
"# Copyright 2018-2020 Xanadu Quantum Technologies Inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by ap... | [
[
"numpy.array",
"torch.from_numpy",
"torch.as_tensor"
],
[
"numpy.diag",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AdrienCorenflos/blackjax | [
"e3204e17d3833be73d6f703dbb33b68b1431e629"
] | [
"tests/test_tempered_smc.py"
] | [
"\"\"\"Test the tempered SMC steps and routine\"\"\"\nfrom typing import List\n\nimport jax\nimport jax.numpy as jnp\nimport jax.scipy.stats as stats\nimport numpy as np\nimport pytest\n\nimport blackjax.hmc as hmc\nimport blackjax.inference.smc.resampling as resampling\nimport blackjax.inference.smc.solver as solv... | [
[
"numpy.diag",
"numpy.log",
"numpy.random.seed",
"numpy.triu_indices",
"numpy.logspace",
"numpy.random.exponential",
"numpy.eye",
"numpy.random.normal",
"numpy.random.randn",
"numpy.random.rand",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
atom-zh/SA_Classification | [
"7036d00d2505a26f5bc08b126fad0b37bb8e51b4"
] | [
"mTextCNN/graph.py"
] | [
"# -*- coding: UTF-8 -*-\n# !/usr/bin/python\n# @time :2019/6/3 10:51\n# @author :Mo\n# @function :graph of base\n\n\nfrom keras.layers import Reshape, Concatenate, Conv2D, MaxPool2D\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras_l... | [
[
"tensorflow.clip_by_value",
"tensorflow.multiply",
"tensorflow.constant",
"tensorflow.nn.sigmoid",
"tensorflow.nn.softmax",
"tensorflow.range",
"tensorflow.reduce_sum",
"tensorflow.python.ops.array_ops.where",
"tensorflow.cast",
"tensorflow.python.ops.array_ops.zeros_like",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
marc-ortuno/Vocal-Percussion-Classification-for-Real-Time-Context | [
"e7ed1f13cc1868a824f4036dd08ec6bed4266c08"
] | [
"Prototype/core/rt_test.py"
] | [
"#!/usr/bin/env python3\n\"\"\"Plot the live microphone signal(s) with matplotlib.\nMatplotlib and NumPy have to be installed.\n\"\"\"\nprint(\"Initialising... please wait\")\n\nimport tkinter as tk\nimport numpy as np\nimport sys\nimport sounddevice as sd\nfrom app import main, process, init, init_activity_detecti... | [
[
"numpy.asarray",
"numpy.array",
"pandas.read_csv",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mjasnikovs/horus | [
"c342aafc074e163a5a2eaa3564cba3131c6050a0"
] | [
"cncreader.py"
] | [
"import cv2 as cv\nimport csv\nimport re\nimport numpy as np\nimport argparse\nimport serial\n\nfrom helper import webcamStream, downScaleImage, RGB\nfrom os import listdir, path\nfrom pyzbar.pyzbar import decode\nfrom threading import Timer\n\nCSV_DIR = './CSV'\nCSV_LIST = dict()\n\nparser = argparse.ArgumentParse... | [
[
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sreekarchigurupati/fury | [
"07ed10123954d3ce3197c45f8ee54a7d4909656d",
"07ed10123954d3ce3197c45f8ee54a7d4909656d",
"07ed10123954d3ce3197c45f8ee54a7d4909656d"
] | [
"fury/tests/test_actors.py",
"docs/tutorials/04_stream/viz_interaction.py",
"fury/shaders/tests/test_base.py"
] | [
"import os\nimport itertools\nfrom tempfile import TemporaryDirectory as InTemporaryDirectory\n\nimport pytest\nimport numpy as np\nimport numpy.testing as npt\nfrom scipy.ndimage.measurements import center_of_mass\nfrom scipy.signal import convolve\n\nfrom fury import shaders\nfrom fury import actor, window, primi... | [
[
"numpy.diag",
"numpy.linspace",
"matplotlib.pyplot.plot",
"scipy.ndimage.measurements.center_of_mass",
"numpy.testing.assert_equal",
"numpy.eye",
"numpy.testing.assert_almost_equal",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"numpy.zeros",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Fawkes7/mmdetection3d | [
"7d5a339c699a73b3c8d65de5e48c7407f8640b23",
"7d5a339c699a73b3c8d65de5e48c7407f8640b23",
"7d5a339c699a73b3c8d65de5e48c7407f8640b23"
] | [
"github_code/mmdetection3d/mmdet3d/models/backbones/sparse_unet.py",
"process_partnet_data.py",
"github_code/mmdetection3d/mmdet3d/ops/pvconv/se.py"
] | [
"import torch, torch.nn as nn\nfrom mmdet3d.ops import spconv\nfrom mmdet3d.ops.spconv.modules import SparseModule\nimport functools\nfrom collections import OrderedDict\n\n\nclass ResidualBlock(SparseModule):\n def __init__(self, in_channels, out_channels, norm_fn, indice_key=None):\n super().__init__()\... | [
[
"torch.nn.ReLU",
"torch.nn.Identity",
"torch.cat"
],
[
"numpy.concatenate",
"numpy.arange",
"numpy.array"
],
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.Sigmoid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amitnyc83/perspective | [
"013cd59b00187257026dc78e229b21d19acd0d98"
] | [
"python/perspective/perspective/tests/table/test_table_pandas.py"
] | [
"# *****************************************************************************\n#\n# Copyright (c) 2019, the Perspective Authors.\n#\n# This file is part of the Perspective library, distributed under the terms of\n# the Apache License 2.0. The full license can be found in the LICENSE file.\n#\nimport six\nfrom p... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.random.randn",
"pandas.Period",
"numpy.array",
"pandas.Timestamp",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
eldridgejm/gradelib | [
"f94eebe0c292c4e627b8450a554431b4d9d4abeb"
] | [
"tests/test_gradebook.py"
] | [
"import pathlib\n\nimport pytest\nimport pandas as pd\nimport numpy as np\n\nimport gradelib\n\n\nEXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / \"examples\"\nGRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(\n EXAMPLES_DIRECTORY / \"gradescope.csv\"\n)\nCANVAS_EXAMPLE = gradelib.Gradebook.from_canv... | [
[
"pandas.Series",
"pandas.DataFrame",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
SayHiRay/tensorflow | [
"abd645085b1dd1496df847b05a1934d471a2f2c0",
"abd645085b1dd1496df847b05a1934d471a2f2c0"
] | [
"tensorflow/python/estimator/estimator.py",
"tensorflow/contrib/autograph/impl/conversion_test.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.training.monitored_session.Scaffold",
"tensorflow.python.training.warm_starting_util.warm_start",
"tensorflow.python.training.training.LoggingTensorHook",
"tensorflow.python.training.training.latest_checkpoint",
"tensorflow.python.estimator.export.export.get_temp_export_dir"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
NithishC/Facial-Detection-in-ATM-Bank | [
"db6fffae7ea5fbf7471c1b1e4145ebcd424a0a2c"
] | [
"ATM-BANK/faces-train.py"
] | [
"import cv2\nimport os\nimport numpy as np\nfrom PIL import Image\nimport pickle\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nimage_dir = os.path.join(BASE_DIR, \"images\")\n\nface_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')\nrecognizer = cv2.face.LBPHFaceRecognize... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DmitrySerg/top-russian-music | [
"0e8474d274c7ab5eda552121133c051827695205"
] | [
"models/lda_model.py"
] | [
"\nimport pandas as pd # Пакет для работы с таблицами\nimport numpy as np # Пакет для работы с векторами и матрицами\n\n# Из библиотеки для работы с текстами вытащим \n# методы для предобработки и модели\nfrom gensim import corpora, models\nfrom gensim.models.callbacks import PerplexityM... | [
[
"pandas.read_csv",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
qq1440837150/DeepSpeech | [
"068c2b1417dde3adcd49ddfb24da987c940db2e3"
] | [
"deepspeech/frontend/audio.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"scipy.signal.fftconvolve",
"numpy.arange",
"numpy.cumsum",
"numpy.dtype",
"numpy.finfo",
"numpy.concatenate",
"numpy.frombuffer",
"numpy.log10",
"numpy.mean",
"numpy.any",
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
jkkl/sccl | [
"5811042f48a5cb500a99834d4390cb2ef012386b"
] | [
"utils/kmeans.py"
] | [
"\"\"\"\nCopyright Amazon.com, Inc. or its affiliates. All Rights Reserved\n\nAuthor: Dejiao Zhang (dejiaoz@amazon.com)\nDate: 02/26/2021\n\"\"\"\n\nimport torch\nimport numpy as np\nfrom utils.metric import Confusion\nfrom sklearn.cluster import KMeans\n\ndef get_kmeans_centers(embedder, train_loader, num_classes)... | [
[
"numpy.concatenate",
"torch.cat",
"sklearn.cluster.KMeans",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KarateJB/Python.Practice | [
"a5f00f669dc4b815601c093ce0753a0a82b4328a"
] | [
"src/DeepLearn/venv/Lab/dataset/mnist.py"
] | [
"\"\"\"\nForked from https://github.com/oreilly-japan/deep-learning-from-scratch\nAuthor: 齊藤康毅\n\"\"\"\n# coding: utf-8\ntry:\n import urllib.request\nexcept ImportError:\n raise ImportError('You should use Python 3.x')\nimport os.path\nimport gzip\nimport pickle\nimport os\nimport numpy as np\n\n\n# MNIST da... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samuela/jax | [
"4f5547dd85596bff775cc1ed42c013321833a8b6"
] | [
"tests/api_test.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.arange",
"numpy.eye",
"numpy.float16",
"numpy.cos",
"numpy.sin",
"numpy.all",
"numpy.ones",
"numpy.random.randn",
"numpy.random.rand",
"numpy.float32",
"numpy.testing.assert_allclose",
"numpy.exp",
"numpy.shape",
"numpy.tri",
"numpy.array",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tphanmt/StarlinkML | [
"5cb4ddd316628168245b31b430a585639a5672e9"
] | [
"Parser.py"
] | [
"import os\nimport numpy as np\nimport shutil\nimport zipfile\nimport multiprocessing\nfrom tslearn.utils import to_time_series_dataset\n\ndef parseFilesInFolder(filename: str, \n zipFilePath: str,\n out_dir = '/home1/08550/qz3485/workspace/output_data/'\n ... | [
[
"numpy.save",
"numpy.argwhere",
"numpy.concatenate",
"numpy.delete",
"numpy.cross",
"numpy.savetxt",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Milos9304/qiskit-aqua | [
"3bc5de786f4504b9eb5cd58fc7d8285141b0336d",
"3bc5de786f4504b9eb5cd58fc7d8285141b0336d"
] | [
"qiskit/aqua/circuits/phase_estimation_circuit.py",
"qiskit/aqua/operators/legacy/weighted_pauli_operator.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2018, 2020.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.logical_not"
],
[
"numpy.logical_not",
"numpy.absolute",
"numpy.imag",
"numpy.sqrt",
"numpy.asarray",
"numpy.around",
"numpy.stack",
"numpy.concatenate",
"numpy.delete",
"numpy.real",
"numpy.zeros",
"numpy.vdot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pordyna/openPMD-viewer | [
"f7b792be58d5dca1af5b36d9875b3d7768a3617d"
] | [
"openpmd_viewer/openpmd_timeseries/data_reader/h5py_reader/params_reader.py"
] | [
"\"\"\"\nThis file is part of the openPMD-viewer.\n\nIt defines a function that can read standard parameters from an openPMD file.\n\nCopyright 2015-2016, openPMD-viewer contributors\nAuthors: Remi Lehe, Axel Huebl\nLicense: 3-Clause-BSD-LBNL\n\"\"\"\n\nimport h5py\nimport numpy as np\nfrom .utilities import is_sca... | [
[
"numpy.uint32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kjr21362/CarND-Capstone | [
"75043ea100b8b13dc66fe0543284981454d66d25"
] | [
"ros/src/tl_detector/light_classification/tl_classifier.py"
] | [
"from styx_msgs.msg import TrafficLight\nimport cv2\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport rospy\n\nclass TLClassifier(object):\n def __init__(self):\n #TODO load classifier\n self.light = TrafficLight.UNKNOWN\n self.category_index = {1: {'id': 1, 'name': 'Green'}, 2... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.Session",
"tensorflow.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
piloubazin/nighres | [
"9708ac293c04c01ac59467f475f9377015b7ce89"
] | [
"nighres/microscopy/stack_intensity_mapping.py"
] | [
"import numpy as np\nimport nibabel as nb\nimport os\nimport sys\nimport nighresjava\nfrom ..io import load_volume, save_volume\nfrom ..utils import _output_dir_4saving, _fname_4saving, \\\n _check_topology_lut_dir, _check_available_memory\n\n\ndef stack_intensity_mapping(image, references, mappe... | [
[
"numpy.nanmax",
"numpy.nanmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LikeJulia/nni | [
"611ed639bb2ecd1db5a0241833c0d0a1e7812b1d",
"611ed639bb2ecd1db5a0241833c0d0a1e7812b1d"
] | [
"nni/algorithms/hpo/gridsearch_tuner.py",
"examples/model_compress/pruning/v2/fpgm_pruning_torch.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\n\"\"\"\nGrid search tuner.\n\nFor categorical parameters this tuner fully explore all combinations.\nFor numerical parameters it samples them at progressively decreased intervals.\n\"\"\"\n\n__all__ = ['GridSearchTuner']\n\nimport logging... | [
[
"numpy.prod",
"scipy.special.erfinv"
],
[
"torch.nn.CrossEntropyLoss",
"torch.randn",
"torch.no_grad",
"torch.rand",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.4",
"0.16",
"0.19",
"0.18",
"1.2",
"0.12",
"1.0",
"0.17",
"1.3"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
... |
YuWVandy/Coupled-Transportation-and-Power-System | [
"f338648df3da78ed24e7797bf85e7182cf57768b"
] | [
"System.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 20 20:43:07 2020\n\n@author: wany105\n\"\"\"\nimport ShareFunction as sf\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nclass system(object):\n \"\"\"System object: network of networks\n \"\"\"\n def __init__(self, name, networks, inters = ... | [
[
"numpy.isnan",
"numpy.arange",
"numpy.ones",
"numpy.copy",
"numpy.random.rand",
"numpy.meshgrid",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ordabayevy/pyro | [
"ced727e579c50654bdf264604652d93fe663c0d5",
"ced727e579c50654bdf264604652d93fe663c0d5"
] | [
"pyro/distributions/logistic.py",
"tests/infer/reparam/test_loc_scale.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nimport torch\nfrom torch.distributions import constraints\nfrom torch.distributions.utils import broadcast_all\nfrom torch.nn.functional import logsigmoid\n\nfrom .torch_distribution import TorchDistribution\n\n\nclass SkewLogi... | [
[
"torch.distributions.utils.broadcast_all",
"torch.Size",
"torch.nn.functional.logsigmoid"
],
[
"torch.stack",
"torch.empty",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mahnooranjum/Regression_MachineLearning | [
"37c2bc7ef7815997cfa59816effd22f58cfd8f13",
"37c2bc7ef7815997cfa59816effd22f58cfd8f13"
] | [
"R7-Ridge.py",
"Regression_Evaluation/R16_TheilSenRegressor.py"
] | [
"##############################################################################\n#\n# Mahnoor Anjum\n# manomaq@gmail.com\n# \n# References:\n# Official Documentation\n#\n##############################################################################\n# Importing the libraries\nimport numpy as np\ni... | [
[
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"sklearn.linear_model.Ridge",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"pandas.read_csv",
"sklearn.linear_model.TheilSenRegressor",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
v-wangg/stochastic-neural-architecture-search | [
"235020db9f737f5507d4ec8add973bc2618c5ac3"
] | [
"model_search.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom operations import *\nfrom torch.autograd import Variable\nfrom genotypes import PRIMITIVES\nfrom genotypes import Genotype\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\nclass MixedOp(nn.Module):\n ## Equatio... | [
[
"torch.cat",
"torch.randn",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ucfnlp/sent-fusion-transformers | [
"0b84ba6ce0a0c8c383817c5c32cd1563b362f047"
] | [
"bert/modeling.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unl... | [
[
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.assert_less_equal",
"tensorflow.truncated_normal_initializer",
"tensorflow.squeeze",
"tensorflow.train.list_variables",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
OpenXAIProject/OneShot-Learning-Selective-Ensemble | [
"10314d9da3b4618198c58b6f1e670116b762f09d"
] | [
"src/test.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport argparse\n\nimport _init_paths\nfrom utils import Avg, tf_acc, to1hot, cross_entropy, get_proto_model_output\nfrom networks import Baseline_CNN\nfrom dataset import MiniImageNet\nfrom episode import Episode_Generator\n\ndef parse_args():\n parser = argparse.Ar... | [
[
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"numpy.mean",
"tensorflow.Session",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ckeokot/Plot3D_utilities | [
"7bba70aeb48d8577ff582e999e8ce186c68d0189"
] | [
"python/plot3d/split_block.py"
] | [
"'''\nSplit blocks is combination of help from Dave Rigby and Tim Beach \n'''\n\nfrom .face import Face\nfrom .block import Block\nfrom typing import List\nfrom enum import Enum\nfrom math import gcd, sqrt \nimport numpy as np \n\nclass Direction(Enum):\n i = 0\n j = 1\n k = 2\n\ndef max_aspect_ratio(X:np.... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siddharthbharthulwar/nCov-Tracker | [
"1be723cc098897c4fb78fdfb47da07f7104b8b03"
] | [
"Regression/functions.py"
] | [
"import numpy as np \n\ndef logistic(x, a, b, r):\n\n return a / (1 + (b * np.exp(-1 * r * x)))\n\ndef exponential(x, a, k, b):\n return a*np.exp(x*k) + b\n\ndef logisticDistribution(x, a, b, r):\n\n return (a * r * b * np.exp(-1 * r * x)) / ((1 + (b * np.exp(-1 * r * x))) ** 2)\n\n"
] | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yenzmike/dynProg | [
"4d3212a788de2993d06f8eaa7756f0b2e109815b"
] | [
"examples/example_1.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom hydropt import Basin, Outflow, Turbine, PowerPlant, \\\n Standing, MinPower, MaxPower, Scenario, Underlyings\n\n\nbasins = [\n Basin(\n name='Mühlensee',\n volume=81*3600,\n num_states=81,\n levels=(2000, 2120),\n ... | [
[
"matplotlib.pyplot.legend",
"numpy.arange",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.datetime64",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FengNiMa/pytorch_diffusion_model_celebahq | [
"b81e57453066e05d71feb8451bbff766df401386"
] | [
"UNet.py"
] | [
"import os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom util import calc_t_emb, flatten\n\ndef swish(x):\n return x * torch.sigmoid(x)\n\nclass Upsample(nn.Module):\n def __init__(self, n_channels, with_conv=True):\n super(Upsample, self).__init__()\n self.with... | [
[
"torch.nn.Softmax",
"torch.sigmoid",
"torch.zeros",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.Upsample",
"torch.bmm",
"torch.nn.GroupNorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dukomoran/PESFM | [
"19750c8b7f2feab5cac4ab3db0a38a11c476f8f4"
] | [
"code/utils/sparse_utils.py"
] | [
"import torch\n\n\nclass SparseMat:\n def __init__(self, values, indices, cam_per_pts, pts_per_cam, shape):\n assert len(shape) == 3\n self.values = values\n self.indices = indices\n self.shape = shape\n self.cam_per_pts = cam_per_pts\n self.pts_per_cam = pts_per_cam\n ... | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rbrishabh/jax | [
"61e52ab2b5f3a7f593e15301f54ad142b8d44718"
] | [
"jax/lax_linalg.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.issubdtype",
"numpy.array",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
midas-research/dlkp | [
"5f47a780a6b05a71f799287d8ad612542a897047"
] | [
"src/dlkp/generation/trainers.py"
] | [
"import imp\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\nfrom torch.utils.data import Dataset\n\nimport transformers\nfrom transformers import Trainer\nfrom transformers.trainer_utils import (\n get_last_checkpoint,\n is_main_process,\n PredictionOutput... | [
[
"torch.no_grad",
"torch.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
congqixia/pymilvus-orm | [
"788fd5577aa2f5e4696497e4f521841fb6466a88"
] | [
"tests/utils.py"
] | [
"import random\nimport pandas\nfrom sklearn import preprocessing\nfrom pymilvus_orm.types import DataType\n\ndefault_dim = 128\ndefault_nb = 1200\ndefault_nq = 10\ndefault_float_vec_field_name = \"float_vector\"\n\nall_index_types = [\n \"FLAT\",\n \"IVF_FLAT\",\n \"IVF_SQ8\",\n \"IVF_SQ8_HYBRID\",\n ... | [
[
"sklearn.preprocessing.normalize",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MengyuanChen21/CVPR2022-FTCL | [
"907712ec55d3afa9a61095f850aafa346b1f0987"
] | [
"train_thu.py"
] | [
"from tqdm import tqdm\r\nimport numpy as np\r\nimport torch\r\n\r\n\r\ndef train(args, model, dataloader, pair_dataloader, criterion, optimizer):\r\n model.train()\r\n print(\"-------------------------------------------------------------------------------\")\r\n device = args.device\r\n\r\n # train_pro... | [
[
"torch.isnan",
"torch.no_grad",
"numpy.mean",
"numpy.zeros_like",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vintageGV/tensorflow | [
"cf9818e1725d8f6229c1865eb26549725c04959a"
] | [
"tensorflow/python/training/monitored_session.py"
] | [
"# pylint: disable=g-bad-file-header\n# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/l... | [
[
"tensorflow.core.protobuf.config_pb2.RunMetadata",
"tensorflow.python.training.basic_session_run_hooks.StepCounterHook",
"tensorflow.python.ops.resources.report_uninitialized_resources",
"tensorflow.core.protobuf.config_pb2.RunOptions",
"tensorflow.python.training.basic_session_run_hooks.Summa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
flygod1159/cudf | [
"48f4e96adf8110b11af98a04b360e7287c5cff29"
] | [
"python/cudf/cudf/tests/test_series.py"
] | [
"# Copyright (c) 2020-2021, NVIDIA CORPORATION.\n\nimport operator\nimport re\nfrom string import ascii_letters, digits\n\nimport cupy as cp\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pytest\n\nimport cudf\nfrom cudf.testing._utils import (\n NUMERIC_TYPES,\n TIMEDELTA_TYPES,\n ... | [
[
"pandas.Series",
"pandas.Int16Dtype",
"pandas.RangeIndex",
"pandas.Int32Dtype",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.random.randint",
"numpy.arange",
"numpy.int8",
"pandas.Index",
"pandas.StringDtype",
"numpy.repeat",
"numpy.zeros",
"p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geresdi/qtplot | [
"6668fba4c1f25fcdac806070031867dd4ab4751c"
] | [
"qtplot/qtplot.py"
] | [
"from __future__ import print_function\n\nfrom six.moves import configparser\nimport numpy as np\nimport os\nimport logging\nimport sys\nfrom collections import OrderedDict\n\nfrom PyQt4 import QtGui, QtCore\n\nfrom .colormap import Colormap\nfrom .data import DatFile, Data2D\nfrom .export import ExportWidget\nfrom... | [
[
"numpy.isnan",
"numpy.nanmax",
"numpy.nanmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
coffeeshaychildren/master-computing-upload | [
"e9352d0d52f40ef022c74ae01ca9e03395bdf860"
] | [
"tensorflow_model_optimization/python/core/internal/tensor_encoding/testing/test_utils.py"
] | [
"# Copyright 2019, The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"tensorflow.sign",
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.get_default_graph",
"tensorflow.nest.flatten",
"tensorflow.Graph",
"tensorflow.floor",
"numpy.stack",
"tensorflow.to_float",
"tensorflow.tile",
"tensorflow.norm",
"tensorflow.is_tensor",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ayjabri/playGym | [
"519768da85ff755878200ef6cca74b0e2858c273",
"519768da85ff755878200ef6cca74b0e2858c273"
] | [
"Bowling/lib/common.py",
"LunarLander/04_lander_a3c.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom types import SimpleNamespace\n\nSEED = 123\n\nHYPERPARAMS = {\n 'pong': SimpleNamespace(**{\n 'env_name': \"PongNoFrameskip-v4\",\n 'stop_reward': 18.0,\n 'run_name': 'pong',\n 'replay_size': 1000... | [
[
"torch.BoolTensor",
"torch.tensor",
"torch.no_grad",
"numpy.array",
"torch.nn.MSELoss"
],
[
"torch.multiprocessing.set_start_method",
"torch.LongTensor",
"torch.nn.functional.softmax",
"torch.multiprocessing.Queue",
"torch.nn.functional.log_softmax",
"torch.cat",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Stormii2018/FaceReID | [
"a3bde7e8c47eabce8a83890068f350bb747aab17",
"a3bde7e8c47eabce8a83890068f350bb747aab17"
] | [
"Train/torchreid/losses/distill_loss.py",
"Train/torchreid/losses/__init__.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass DistillLoss(nn.Module):\n def __init__(self, teacher_model):\n super(DistillLoss, self).__init__()\n self.teacher = teacher_model\n self.criterion = nn.L1Loss()\n\n def forward(self, features, **kwargs):\n imgs = kwargs['imgs']\n ... | [
[
"torch.no_grad",
"torch.nn.L1Loss"
],
[
"torch.nn.DataParallel",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anirban-code-to-live/diverse-clustering | [
"d0935b0094b1f50f9a99403f119b9f0c1795223d"
] | [
"src/baseline/DivSimGrouping.py"
] | [
"import numpy as np\nfrom sklearn.cluster import KMeans\n\n\nclass DivSimGrouping:\n\n def __init__(self, adjacency_mat, groups=2):\n self._adj_mat = adjacency_mat\n self._group_count = groups\n self._node_count = adjacency_mat.shape[0]\n # print('Adjacency matrix dimension :: ', adja... | [
[
"sklearn.cluster.KMeans",
"numpy.matmul",
"numpy.ones",
"numpy.linalg.eigh",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adldotori/C0pyWriter | [
"8dae795e18c67118559b45c83f41ff9f47643a2e"
] | [
"backend/gpt2/src/sample.py"
] | [
"import tensorflow as tf\n\nimport gpt2.src.model as model\n\ndef top_k_logits(logits, k):\n if k == 0:\n # no truncation\n return logits\n\n def _top_k():\n values, _ = tf.nn.top_k(logits, k=k)\n min_values = values[:, -1, tf.newaxis]\n return tf.where(\n logits ... | [
[
"tensorflow.TensorShape",
"tensorflow.nn.softmax",
"tensorflow.fill",
"tensorflow.gather_nd",
"tensorflow.range",
"tensorflow.concat",
"tensorflow.sort",
"tensorflow.equal",
"tensorflow.ones_like",
"tensorflow.cast",
"tensorflow.nn.top_k",
"tensorflow.name_scope",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
SteffenMeinecke/pandapipes | [
"2d0631c053735e4116a145bae9975379135b9c36",
"2d0631c053735e4116a145bae9975379135b9c36"
] | [
"pandapipes/component_models/ext_grid_component.py",
"pandapipes/properties/properties_toolbox.py"
] | [
"# Copyright (c) 2020-2022 by Fraunhofer Institute for Energy Economics\n# and Energy System Technology (IEE), Kassel, and University of Kassel. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.\n\nimport numpy as np\nfrom numpy import dtype\n\... | [
[
"numpy.ones_like",
"numpy.unique",
"numpy.dtype",
"numpy.concatenate",
"numpy.array",
"numpy.isin"
],
[
"numpy.shape",
"numpy.sqrt",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnhaaD/DarwinexLabs | [
"06e3722603d789a02ca0224cda8c26a3eb47ee43"
] | [
"tools/Python/MetaTrader_Helpers/Data_Processing/DWX_HISTORY_IO_v2_0_1_RC8.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n\r\n DWX_HISTORY_IO_v2_0_1_RC8.py\r\n --\r\n @author: Darwinex Labs (www.darwinex.com)\r\n \r\n Copyright (c) 2017-2019, Darwinex. All rights reserved.\r\n \r\n Licensed under the BSD 3-Clause License, you may not use this file except \r\n in complia... | [
[
"pandas.set_option",
"pandas.DataFrame.from_dict"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gr82morozr/realtime_graph | [
"1bc04d1a42e318026e6e82ab980fd6265e19e522"
] | [
"archives/demo.GLScatterPlotItem.py"
] | [
"from pyqtgraph.Qt import QtCore, QtGui\nimport pyqtgraph.opengl as gl\nimport numpy as np\n\napp = QtGui.QApplication([])\nw = gl.GLViewWidget()\nw.show()\ng = gl.GLGridItem()\nw.addItem(g)\n\n#generate random points from -10 to 10, z-axis positive\npos = np.random.randint(-10,10,size=(1000,3))\npos[:,2] = np.abs(... | [
[
"numpy.abs",
"numpy.arange",
"numpy.zeros",
"numpy.roll",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mikarubi/segmentation | [
"33059b969761f35d04dd40073b1d86fded100ffb"
] | [
"voluseg/_steps/step4a.py"
] | [
"def define_blocks(lx, ly, lz, n_cells_block, n_voxels_cell, volume_mask):\n '''get coordinates of individual blocks'''\n\n import os\n # disable numpy multithreading\n os.environ['OMP_NUM_THREADS'] = '1'\n os.environ['MKL_NUM_THREADS'] = '1'\n os.environ['NUMEXPR_NUM_THREADS'] = '1'\n os.envir... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.ones",
"numpy.cbrt",
"numpy.max",
"numpy.any",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thermokarst-forks/emperor | [
"a93f029548c421cb0ba365b4294f7a5a6b0209ce"
] | [
"tests/test_parse.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright (c) 2013--, emperor development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file LICENSE.md, distributed with this software.\n# -------------------------------------... | [
[
"numpy.testing.assert_almost_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cholazzzb/APF_Swarm_Control_Simulator | [
"a58a1f55cd709f12928cc31d2320f7833d761c50"
] | [
"python_code/main_agent_obstacle_target.py"
] | [
"from Agent import Agent\nfrom Obstacle import Obstacle\nfrom ObstaclePotentialField import ObstaclePotentialField\nfrom Target import Target\nfrom TargetPotentialField import TargetPotentialField\nfrom Simulator import Simulator\n\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport nu... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
likun-stat/scalemixture_py | [
"2339ba9eb8588aa3f043a89f910fea6cbb14586e"
] | [
"integrate.py"
] | [
"import numpy as np\nimport math\nimport os, ctypes\nfrom scipy import LowLevelCallable\nimport scipy.integrate as si\nfrom scipy.stats import norm\nfrom scipy.stats import uniform\nfrom scipy.special import gamma, kv\n# from scipy.stats import uniform\nimport scipy.interpolate as interp\nfrom scipy.stats import ge... | [
[
"numpy.diag",
"scipy.stats.norm.ppf",
"scipy.stats.norm.cdf",
"numpy.sqrt",
"numpy.linspace",
"numpy.concatenate",
"numpy.all",
"numpy.max",
"scipy.stats.uniform.rvs",
"numpy.any",
"numpy.exp",
"scipy.stats.genextreme.logcdf",
"scipy.stats.genextreme.cdf",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jialinwu17/caption_vqa | [
"cbc767541667e9bb32760ac7cd2e822eff232ff5"
] | [
"tools/test_detection_features_converter.py"
] | [
"\"\"\"\nReads in a tsv file with pre-trained bottom up attention features and\nstores it in HDF5 format. Also store {image_id: feature_idx}\n as a pickle file.\n\nHierarchy of HDF5 file:\n\n{ 'image_features': num_images x num_boxes x 2048 array of features\n 'image_bb': num_images x num_boxes x 4 array of bound... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boringlee24/models | [
"a9d436b2f4f495d29ac472dd7d55d0423806ea64"
] | [
"official/utils/misc/keras_utils.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.ConfigProto",
"tensorflow.keras.backend.set_session",
"tensorflow.compat.v1.logging.warn",
"tensorflow.compat.v1.enable_eager_execution",
"tensorflow.python.eager.profiler.stop",
"tensorflow.python.eager.profiler.save",
"tensorflow.compat.v1.logging.info",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huangzhii/CoxNMF | [
"5d25e82ff47d7cf0e7a56cd26355630f21fe3a0c"
] | [
"main.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport time\nimport datetime\nimport copy\nimport warnings\nfrom conf import conf\nfrom tqdm import tqdm\nfrom utils import *\nfrom dataset import getdata, generate_synthetic_data\nfrom models import run_model,... | [
[
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
joelwembo/expert-python-dts-algorithms | [
"d6fc781ea48e96255fcd90fbbe3b9e7c3da60841"
] | [
"quantum/deutsch_jozsa.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nDeutsch-Josza Algorithm is one of the first examples of a quantum\nalgorithm that is exponentially faster than any possible deterministic\nclassical algorithm\n\nPremise:\nWe are given a hidden Boolean function f,\nwhich takes as input a string of bits, and returns either 0 or 1:\n\... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ryanlindeborg/avalanche | [
"32333776e729bad22f369f8923bc32416c9edcf9"
] | [
"examples/lwf_mnist.py"
] | [
"import torch\nimport argparse\nfrom avalanche.benchmarks import SplitMNIST\nfrom avalanche.training.strategies import LwF\nfrom avalanche.models import SimpleMLP\nfrom avalanche.evaluation.metrics import ExperienceForgetting, \\\n accuracy_metrics, loss_metrics\nfrom avalanche.logging import InteractiveLogger\n... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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